Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning

Understand

The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance.

• However, any requirement for a human to manually 'teach' the model is somewhat antithetical to the goals of reinforcement learning.
• This paper attempts to minimize human involvement in the learning process while retaining the performance advantages by using a single human example collected through a simple-to-use virtual reality simulation to assist with RL training.
• Our method augments a single demonstration to generate numerous human-like demonstrations that, when combined with Deep Deterministic Policy Gradients and Hindsight Experience Replay (DDPG + HER) significantly improve training time on simple tasks and allows the agent to solve a complex task (block stacking) that DDPG + HER alone cannot solve.